January 2021: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March 2020 through January 2021

As previously discussed in the first of this blog series on April 13, 2020, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) and a select set of European countries to build a picture of the load impacts by region. To assess the load impact of COVID-19 mitigation strategies, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Across Europe and North America, the biggest estimated load reductions occurred in April 2020 with an estimated reduction in average daily load between -12.3% and -7.2%. This instance of the memos extends prior analyses that presents estimates of the load impacts by region, month, and the time of use period by adding pre- and post-hourly load shapes by season. A comparison of the hourly shapes provides a deeper understanding of how power consumption is evolving given current economic conditions and COVID-19 restrictions.

For a detailed summary of the estimated load impacts, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting team will continue to post updated summary blogs and corresponding memos on these trends. Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.


Snow Your Enemy

I have lived in New England my entire life. You may be unaware of this fact, but it snows here sometimes.

Snow can be wonderful. As a child, I shoveled my fair share of driveways. I delivered newspapers in the snow. And many times, I walked through a neighbor’s yard and climbed through a hole in the fence to get to a nearby school, which had a perfect hill for sledding.

Notwithstanding those idyllic images, snow creates unique challenges for solar generation forecasting.
There are two broad categories of solar forecasting. The first category is behind-the-meter (BTM) solar generation, which typically consists of rooftop solar panels. BTM has its own set of challenges including lack of visibility into the data itself. In most cases, the amount of energy that is generated, consumed, and/or sent back to the grid is not directly available to grid operators (at the Independent System Operator level) and it may or may not be available to utility distribution system operators. Because the data is often unavailable, the danger is that grid operators may over-commit generation resources on sunny days (when the BTM is generating a lot of power) and under-commit generation resources on cloud days (when the BTM is not generating much power at all).

The second category consists of grid-connected solar plants. In the area where I live, these facilities are often along the sides of highways. The panels tend to be fixed (i.e., they do not track the sun) and they face southward for maximum exposure to the sun as it traverses the southern sky from east to west.

In some cases, the historical generation for these grid-connect solar plants is available to us, along with the system’s total capacity. And, we have access to the useful weather concepts, such as:

  • Drybulb temperature
  • The ambient temperature
  • Cloud cover – the percent of the sky that is covered by clouds, ranging from 0 to 100, where 0 means a totally clear sky and 100 indicates a totally cloudy sky.
  • Solar irradiance – the amount of energy striking a surface on the earth, measured in watts-per-square-meter. The concepts are often Global Horizontal Irradiance (GHI) or Plane of Array (POA). A related concept is the maximum solar irradiance, which can be calculated from the latitude and longitude of a location. This does not account for the presence or absence of clouds, but merely provides the maximum value at the location and time based on a totally clear sky.

We tend to not have data on snow accumulation. Even if we did have that data, I am not sure that it would be especially useful. If you do not live in a snowy region, this may come as a surprise to you – the sky is typically cloudy when snow is falling. That is useful information because the forecast models would perceive a cloudy day and the generation forecast would be lower.

Eventually (and thankfully), the snow stops falling, at which point the sky may become totally clear. The forecast models may then predict a much higher generation forecast. Unfortunately, that forecast is likely to be wrong because of the accumulated snow covering the panels.

I visited a nearby solar array and I took the following photograph a full 24-hours after the snow stopped falling. The sky is essentially clear, but I estimate that these panels are producing exactly 0 kWh of electricity because they are entirely covered with snow. What are the factors that could influence whether the snow melts? Both increasing temperature and clear skies would certainly contribute.

It seems to me that the owners and operators of such facilities would benefit from low-tech solutions to clear the snow. There may be an entirely reasonable explanation as to why these companies do not proactively clear the panels. Maybe there are issues regarding potential damage to the panels? That seems plausible. Maybe it is too expensive and not economically worthwhile? But that seems doubtful.

I submit to you the following figure, which depicts the historical data from a solar plant in New England. I have clipped the energy units from the Y-axis to keep the data anonymous. The figure shows data from Nov. 1, 2020 through Jan. 31, 2021. The most salient point is that the observations in January 2021 barely exceed 0 for weeks, which means they are generating and selling roughly 0 kWh. This is entirely attributable to snow cover.

The days after snow falls create an inherently difficult forecasting problem. We do not know how much snow fell at the actual site of the panels. In fact, we may only have weather data for a station that is miles away. We do not know if the facility clears the panels. Even if the temperature increases sufficiently to melt the snow, we do not know how long that process may take. The temperature may increase sufficiently and then decreases again, thus freezing the partially melted snow.

These are challenging and intractable issues from the perspective of the forecaster. What can we do about this? First, we must adjust our expectations. In snowy regions, we cannot possibly expect to be as accurate in the winter as we are in the summer. Second, we could attempt a ‘persistence model’ which utilizes lagged loads. This will only be useful when real-time data is available, and the accuracy will certainly degrade as we extend into the forecast horizon. Third, we could attempt to code-up some logic regarding the temperature after a snowfall to account for snowmelt, but those relationships are not likely to be sufficiently robust or consistent to provide a useful signal for the model to discern.

Here is the coda to this tale. I visited the same solar array 3-days after the snow stopped falling. I estimate that the panels are roughly 5% clear. In other words, the array looks substantially the same as when I visited 2-days prior.

Snow is to solar panels what kryptonite is to Superman – it takes away all the power.


Summing and Averaging

The Sum function in MetrixND seems like a complex way to make adding difficult.

In a MetrixND transformation, numbers are added by joining variables with the “+” sign. Adding three variables is as simple as writing the following expression in the transformation editor formula box:

DataSource.Variable1 + DataSource.Variable2 + DataSource.Variable3

The complex way to add is using the “Sum” function. This function requires inserting the three variables separated by commas (,) as Sum function parameters, as shown below:

Sum(DataSource.Variable1,DataSource.Variable2,DataSource.Variable3)

Technically speaking, the Sum function requires five extra characters to do the same work as the traditional “+” sign.

So, why does MetrixND include this function?

In the situation where variables have missing values, the calculation using the “+” sign results in a missing value. In other words, a number plus a missing value equals a missing value.

100 + MISSING = MISSING

While the Sum function behaves the same, the Ignore Missing option changes the behavior to produce a value. In other words, the Sum function with the Ignore Missing option selected means that a number plus a missing value equals a number.

100 + MISSING = 100

To activate the Ignore Missing option, check the Ignore Missing box in the transformation editor as shown below.

The Ignore Missing options works with the following functions:

  • Sum
  • Avg
  • Max
  • Min

It doesn’t matter whether you use traditional math operators or the functions when data is complete. However, when the dataset has missing values, the functions and Ignore Missing options may be the difference between forecasting a number and forecasting a MISSING.

Complexity has its purpose.


Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March through December 2020

As previously discussed in the first of this blog series on April 13, 2020, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) and a select set of European countries to build a picture of the load impacts by region. To assess the load impact of COVID-19 mitigation strategies, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Across Europe and North America, the biggest estimated load reductions occurred in April with an estimated reduction in average daily load between -12.3% and -7.2%. In both Europe and North America, December loads ran lower than expected although the impact more than likely reflects a net load reduction associated with the winter holiday season.

For a detailed summary of the estimated load impacts, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on these trends.

Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.


I Get by With a Little Help from My Friends, and Google

It almost goes without saying that most utilities have seen a noticeable deviation in their electricity sales in 2020 due to the pandemic. But the question remains, how long will the deviation persist and what does the path ahead look like? Load forecasters the world over are peering into their crystal balls to try to figure this out.

A big part of the challenge is finding a driver that forecasters can use in their models to help capture the variation in sales due to behavioral changes from various COVID-19 mitigation policies. And if you do find a driver, with any luck you can forecast it without too much heartburn. Coincidentally, Google has been publishing daily anonymized COVID-19 Community Mobility Data that shows deviations from baseline location data by state, county and country from users who have turned on their Location History setting on their mobile devices. A few of my colleagues and I have leveraged this data in our forecast models, and the results have proven quite favorable.

The comprehensive dataset is available for download in CSV format on Google’s mobility data website, free of charge. Just scroll down to “Community Mobility Reports” and click on the “Global CSV” option to download the CSV file. The file is quite large (about 240 MB and counting) with too many records to fully load into Excel, and so I recommend opening it in something like Notepad or Notepad++ and copying and pasting the relevant data into a spreadsheet. You’ll have to do a little wrangling to get the data in a useable form, but surprisingly not much.

This data represents the percentage change in people’s visits to – or time spent – in six categories of places relative to the defined “baseline day,” or median value for that day-of-the-week from the period of Jan. 3 – Feb. 6, 2020. The categories are retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential. To give you a visual, here is what the data for the state of California looks like:

From this visual, we can see there’s a positive percentage change in the residential category and a negative one in workplaces as people shift to spending more time at home and little to no time in their place of work. Retail and transit are also down as people are shopping less and not taking public transportation. Grocery and pharmacy is down as well, but not as much as other categories because people obviously still need to buy food and medications. These percentage deviations appear to have stabilized since June, which makes forecasting this data a little less intimidating.

One thing that pops out from looking at the data is there’s a well-defined day-type pattern (i.e., weekend vs. weekday) for the residential and workplace categories. That is, there’s less of a change on weekends because people were already home and not at work before the pandemic took off. The large spikes are for holidays, as those days reflect a significant change relative to Google’s baseline. Retail also has a day-type pattern, albeit a little less well-defined. For this reason, I found the retail, workplace and residential categories to be the most applicable and useful for predicting loads in this COVID-19 world. And since the data are of daily frequency, you can leverage them in a daily model, or run them through billing cycles and incorporate them into a monthly SAE model.

Going with the latter approach, I started with a “business as usual” Residential SAE model (i.e., one that’s estimated with data through February 2020 so the COVID-19 period data does not influence the model coefficients). What the model shows is that residential use per customer has been higher since April relative to where it should have been subject to the actual weather that occurred.

But incorporating Google’s mobility data into the model helps to close this gap. Moreover, forecasting what we think the percentage changes in the relevant categories will be gives us a better projection for how the rest of the year might shake out.

Undoubtedly, this data is not perfect. For example, the baseline days probably aren’t representative of the true baseline, and Google is aware of this too. And using them certainly won’t remove all of the wrenches this pandemic has thrown into our forecast models. But they just might help to tighten things up and yield a more reasonable load forecast.

Google states that the data will be available for as long as public health officials find them useful, but who knows how long that may be. I don’t think I will try and forecast that. But with any luck, that will be just long enough.

Shout out to the folks in the Operational Forecasting Team at AEMO for calling this data to our attention!


November 2020: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March through November 2020

As previously discussed in the first of this blog series on April 13, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) and a select set of European countries to build a picture of the load impacts by region. To assess the load impact of COVID-19 mitigation strategies, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Across Europe and North America, the biggest estimated load reductions occurred in April with an estimated reduction in average daily load between -12.3% and -7.2%. In both Europe and North America, November marked a slight uptick in the load impact, reflecting renewed lockdown activity driven by a new wave of COVID-19 cases.

For a detailed summary of the estimated load impacts, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on these trends.

Subscribe to our blog to be notified of new posts. Contact us at forecasting@itron.com if you have further questions.


Do Solar Panels Need to be Cleaned?

It seems like my solar photovoltaic (PV) panel monitoring service, Enphase, started sending more emails recently. Or maybe because I recently wrote a blog (Residential Lighting Efficiency Really Does Make a Difference) during my PV true up period, I probably had solar on the brain. I just received an email recommending that I wash my panels before winter, and it made me start to wonder – my car is parked outside and gets filthy super quick, so do I need to climb up on the roof and clean the panels or hire someone to do it? Living in San Diego, it is true that there’s not much rain and the panels have been up there for four years now without a proper bath. It totally makes sense that general dust, dirt and fire-related ash would make them less efficient, right? But why would I do it before winter when it rains more? How on Earth do you wash them? Will I fall off the roof? All of this is a little counter-intuitive to what the dollar amount on my latest true up indicated, and although I looked at my lighting efficiency, I didn’t really look at the solar production over the years. I never considered that there might also be losses due to grime.

Some research indicates that cleaning your solar panels leads to small improvements in output, yet others say you should clean them twice a year. One site even suggested a 35% loss after two years, but it turns out that all of the cleaning recommendations tended to be from solar panel cleaning companies or from quoting stats via cleaning companies. Then I stumbled on a study by the Jacobs School of Engineering at UCSD that made me feel much better about not having given my panels any attention since they were installed. According to their research, due to the angle that the panels are mounted and being on a roof, they found that rain did a fine job of cleaning the panels as long as there are no bird droppings.

Again, having been part of the forecasting team for so long, I also had to look at the data and graph it:

Surprisingly, there has been a slight annual uptick in production (is that global warming?!). In any case, I definitely agree that my data and the research available are in alignment. I’m good with not cleaning my solar panels. If I wanted to increase my production a smidge during the summer, when there isn’t any rain in sight, I could clean them. But I don’t think it’s worth the effort, and hiring someone definitely would not offset the cost.


Join Our Brown Bag: Community Choice Aggregation Load Forecasting

The last Itron Forecasting Brown Bag of 2020 is on Tuesday, Dec. 8 and is entitled "Community Choice Aggregation Load Forecasting." During this free webinar, Andy Sukenik will present some background on Community Choice Aggregation (CCA) and will discuss tips on how to create forecasts for the short and long term.

Participation is free, but prior registration is required. Each seminar lasts approximately one hour, allowing 45 minutes for the presentation and 15 minutes for questions. Seminars start at noon Pacific time. If you cannot attend a seminar or if you missed one, don’t worry! Your registration ensures that a link to the recording will be sent to you automatically approximately one week after the seminar date, plus you will receive a link to watch any of this year’s previous webinars.

Register today at www.itron.com/forecastingworkshops.

Itron’s Forecasting group has conducted webinars on a variety of forecasting and load research-based topics for many years. All of our past webinars were recorded and are available in a YouTube library.


October 2020: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March through October 2020

As previously discussed in the first of this series on April 13, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) and a select set of European countries to build a picture of the load impacts by region. To assess the load impact of COVID-19 mitigation strategies, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Across Europe and North America, the biggest estimated load reductions occurred in April with an estimated reduction in average daily load between -12.3% and -7.2%. In recent months, the combination of reduced lockdown restrictions and weather has led to no apparent load impact in Europe. In contrast, North America loads continue to fall below expectations that are not adjusted for prevailing weather.

For a detailed summary of the estimated load impacts, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on the trends.

Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.


Residential Lighting Efficiency Really Does Make a Difference!

It’s hard to believe that it’s been 4 years since we installed the solar photovoltaic (PV) panels on our house. As a solar customer in the San Diego Gas & Electric (SDG&E) service territory, we don’t get monthly bills, just one true-up bill on the anniversary date of your start date. We are on a November to November true-up period and just received our fourth true up bill and I was a bit surprised.

I wrote a PV blog after our first year of the solar journey, but here’s a quick overview. We live in San Diego in a 1,500 square foot house and my son likes to keep our house at freezer-like conditions. In 2015, our annual electric bill was about $2,700 (9,000 kWh). Summer bills were in the $400-500 range (800-900 kWh) and always hit tier 2 rates. We ended up with a 7.25 kW system with 25 panels and were able to get into the NEM Successor Tariff (Schedule NEM-ST, NEM-ST or NEM 2.0) where non-bypassable charges are assessed. There was a nominal interconnection fee and we were grandfathered into the tiered rates for 5 years after our system went live. We have 1 more year before we need to move to the TOU rates, which will probably prompt another set of blogs.

In 2017, our first solar year, we owed $48 and we added an electric vehicle (EV) to the mix in December. In 2018, we owed $258 which included a $500 SDG&E EV climate credit (EVCC) and I started using a free EV charging station by the office in October. In 2019, I was doing the majority of my EV charging at work and receiving an $850 EVCC from SDG&E, and we ended up with an $800 credit overall. The latest bill had no EVCC and was only $101.26.

I’m not complaining, but I was surprised it wasn’t higher. I have been working from home since March, so there is additional electric load in general; my son added a larger TV to his room that seems to be on all of the time, in addition to his computers; almost all of my EV charging has been at home, albeit less driving in general due to the pandemic; and, it was a really hot summer in San Diego so the air conditioner ran way more than normal, hitting tier 2 rates a few times.

Working with Itron’s Energy Forecasting Group (EFG) residential sector and end-use data for so long made me a little curious about our usage. What had changed? During our remodeling project toward the end of last year, we installed a bunch of new LED canned lights in our main room, replacing a beam of incandescent lighting that is now hardly used. The adjacent kitchen lights were almost always turned on.

This is our beam of incandescent lighting, and you can see one of the new LED lights on the ceiling.

When you add up all those cute little incandescents, it turns out that the beam uses 663 watts. Adding in the old kitchen lights and using a conservative estimate of 6 hours of use per day, translates into roughly 1,600 kWh/year. Holy cow! They looked so cool when we put them up and I knew they would suck a bunch of energy, but I didn’t do the math. That’s more than 4 new refrigerators! Our new set up has 20 new LED canned lights which are rarely all on, but if they were, that would only be 438 kWh/year. Our tier 1 rate is 28 cents per kWh, so that is a minimum of $310 savings for the year.

It is good timing on this analysis because one of our next home projects is to replace our original single-paned windows. The residential geek in me was planning to replace them with overpriced, super high energy-efficient low U-Factor ones with whatever gas inside. I already have a few quotes but I am now reconsidering the efficiency level needed because just switching to more energy efficient lighting has brought us pretty close to break even with the EV. The ENERGY STAR program was founded in 1991, so ANY new window will be an improvement in efficiency from the current windows that were installed in 1986.


Watch: Short-term Load Forecasts that Account for COVID-19 Mitigation Policies

During last week’s virtual Itron Utility Week, did you miss the forecasting team’s presentation from Frank Monforte entitled Short-term Load Forecasts that Account for COVID-19 Mitigation Policies? Frank discussed learning how to improve day-ahead and intraday forecasts in the face of existing lockdowns and the future re-opening of economies. His presentation ties in with the continued COVID-19 mitigation blog series that you may have been following throughout the last few months.

If you missed this discussion, you can still register to watch the session and any of the other great Itron Utility Week presentations available on demand.

Be sure to subscribe to our blog to be notified of new posts. Contact us at forecasting@itron.com if you have further questions.


Through September 2020: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

As previously discussed in the first of this blog series on April 13, as lockdown policies are enacted to help reduce the spread of the coronavirus disease (COVID-19), the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) and a select set of European countries to build a picture of the load impacts by region. To assess the load impact of COVID-19 mitigation strategies, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Across Europe and North America, the biggest estimated load reductions occurred in April with an estimated reduction in average daily load between -12.3% and -7.2%.  In recent months, the combination of the relaxing of lockdown restrictions and hot weather has led to no apparent load impact in North America, but a continued small impact in Europe.

For a detailed summary of the estimated load impacts, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on the trends.

Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.

Estimated Daily Average Energy Impact Wedge


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